Urban Form Exploration¶

In [ ]:
#Import all packages
In [1]:
import pandas as pd
import numpy as np
import datetime as dt
import seaborn as sns
import matplotlib
import os
import matplotlib.pyplot as plt
import matplotlib.dates as md
import pandas as pd
import matplotlib.pyplot as plt #if using matplotlib
import plotly.express as px #if using plotly
import geopandas as gpd
import warnings

import momepy

import warnings

import geopandas
import libpysal
import momepy
import osmnx
import pandas
In [2]:
# set the filepath and load in a shapefile
fp = r'MSOA.geojson'
map_df = gpd.read_file(fp)
map_df.head()
Out[2]:
OBJECTID MSOA11CD MSOA11NM BNG_E BNG_N LONG_ LAT Shape_Leng Shape__Area Shape__Length geometry
0 1 E02000001 City of London 001 532384 181355 -0.093490 51.5156 9651.221144 3.151475e+06 9651.221144 POLYGON ((-0.09650 51.52295, -0.09644 51.52282...
1 2 E02000002 Barking and Dagenham 001 548267 189685 0.138756 51.5865 8306.888230 2.161561e+06 8306.888230 POLYGON ((0.14810 51.59656, 0.14809 51.59640, ...
2 3 E02000003 Barking and Dagenham 002 548259 188520 0.138149 51.5760 9359.512342 2.141515e+06 9359.512342 POLYGON ((0.14847 51.58083, 0.14841 51.58075, ...
3 4 E02000004 Barking and Dagenham 003 551004 186412 0.176828 51.5564 8475.840309 2.492946e+06 8475.840309 POLYGON ((0.18511 51.56480, 0.18479 51.56455, ...
4 5 E02000005 Barking and Dagenham 004 548733 186824 0.144267 51.5607 7321.657104 1.187954e+06 7321.657104 POLYGON ((0.15187 51.56778, 0.15193 51.56775, ...
In [3]:
eng_df = map_df[map_df['MSOA11CD'].str[0]=='E']
In [4]:
len(eng_df)
Out[4]:
6791
In [5]:
map_df.plot(figsize=(2, 2),linewidth=0.01)
Out[5]:
<AxesSubplot: >
In [6]:
eng_df.crs
Out[6]:
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
In [7]:
eng_df.to_crs('EPSG:27700')
Out[7]:
OBJECTID MSOA11CD MSOA11NM BNG_E BNG_N LONG_ LAT Shape_Leng Shape__Area Shape__Length geometry
0 1 E02000001 City of London 001 532384 181355 -0.093490 51.5156 9651.221144 3.151475e+06 9651.221144 POLYGON ((532153.715 182165.158, 532158.262 18...
1 2 E02000002 Barking and Dagenham 001 548267 189685 0.138756 51.5865 8306.888230 2.161561e+06 8306.888230 POLYGON ((548881.317 190819.983, 548881.138 19...
2 3 E02000003 Barking and Dagenham 002 548259 188520 0.138149 51.5760 9359.512342 2.141515e+06 9359.512342 POLYGON ((548958.570 189072.181, 548954.531 18...
3 4 E02000004 Barking and Dagenham 003 551004 186412 0.176828 51.5564 8475.840309 2.492946e+06 8475.840309 POLYGON ((551550.071 187364.710, 551528.648 18...
4 5 E02000005 Barking and Dagenham 004 548733 186824 0.144267 51.5607 7321.657104 1.187954e+06 7321.657104 POLYGON ((549237.064 187627.944, 549241.332 18...
... ... ... ... ... ... ... ... ... ... ... ...
6819 6820 E02006930 Greenwich 037 540126 178333 0.016812 51.4866 8103.297524 8.674410e+05 8103.297524 MULTIPOLYGON (((540659.012 178728.004, 540601....
6820 6821 E02006931 Greenwich 038 538710 177146 -0.004030 51.4763 9351.043860 2.049169e+06 9351.043860 POLYGON ((538710.355 177960.764, 538716.506 17...
6821 6822 E02006932 Liverpool 060 334899 390314 -2.980710 53.4056 12791.918754 1.477077e+06 12791.918754 POLYGON ((335723.013 391178.004, 335737.225 39...
6822 6823 E02006933 Liverpool 061 334647 389248 -2.984270 53.3960 10815.142852 2.824636e+06 10815.142852 POLYGON ((335092.922 390247.027, 335096.012 39...
6823 6824 E02006934 Liverpool 062 334077 390779 -2.993170 53.4097 8241.211993 2.315879e+06 8241.211993 POLYGON ((334674.989 391540.461, 334679.738 39...

6791 rows × 11 columns

In [8]:
eng_df['geometry']
Out[8]:
0       POLYGON ((-0.09650 51.52295, -0.09644 51.52282...
1       POLYGON ((0.14810 51.59656, 0.14809 51.59640, ...
2       POLYGON ((0.14847 51.58083, 0.14841 51.58075, ...
3       POLYGON ((0.18511 51.56480, 0.18479 51.56455, ...
4       POLYGON ((0.15187 51.56778, 0.15193 51.56775, ...
                              ...                        
6819    MULTIPOLYGON (((0.02464 51.49001, 0.02381 51.4...
6820    POLYGON ((-0.00371 51.48360, -0.00363 51.48350...
6821    POLYGON ((-2.96849 53.41349, -2.96827 53.41305...
6822    POLYGON ((-2.97778 53.40505, -2.97773 53.40505...
6823    POLYGON ((-2.98433 53.41662, -2.98426 53.41651...
Name: geometry, Length: 6791, dtype: geometry
In [9]:
eng_df[eng_df['MSOA11CD']=='E02000001']
Out[9]:
OBJECTID MSOA11CD MSOA11NM BNG_E BNG_N LONG_ LAT Shape_Leng Shape__Area Shape__Length geometry
0 1 E02000001 City of London 001 532384 181355 -0.09349 51.5156 9651.221144 3.151475e+06 9651.221144 POLYGON ((-0.09650 51.52295, -0.09644 51.52282...
In [10]:
df = pd.DataFrame()
x=0 #index 0 for MSOA, which is city of London E02000001 to explore in Urban Form
In [11]:
#sample to generate some of the urban form features, this is for 1 feature but then used in a loop later

msoa = eng_df.iloc[x].geometry #retrieve data for index 0 E0200001
msoa_name = eng_df.iloc[x].MSOA11CD 
buildings = osmnx.geometries.geometries_from_polygon(msoa, tags={'building':True}) #retrieve data from polygon geometry
buildings = buildings[buildings.geom_type == "Polygon"].reset_index(drop=True) 
buildings = buildings[["geometry"]].to_crs('EPSG:27700') #set geometry to British Coordinate System
buildings["uID"] = range(len(buildings)) #lenght of buildings in
buildings['MSOA11CD'] = msoa_name #tag as MSOA
        
osm_graph = osmnx.graph_from_polygon(msoa, network_type='drive') #retrieve drive, street data
osm_graph = osmnx.projection.project_graph(osm_graph, to_crs='EPSG:27700') #change to british coordinate 
streets = osmnx.graph_to_gdfs(osm_graph,nodes=False,edges=True, 
                                  node_geometry=False,fill_edge_geometry=True)
streets = momepy.remove_false_nodes(streets)
streets = streets[["geometry"]]
streets["nID"] = range(len(streets)) #street data range
        
limit = momepy.buffered_limit(buildings, 100) 
tessellation = momepy.Tessellation(buildings, "uID", limit, verbose=False, segment=1) #generate tessellation
tessellation = tessellation.tessellation
buildings = buildings.sjoin_nearest(streets, max_distance=1000, how="left") #using building street data
buildings = buildings.drop_duplicates("uID").drop(columns="index_right")
tessellation = tessellation.merge(buildings[['uID', 'nID']], on='uID', how='left')
buildings["area"] = buildings.area
tessellation["area"] = tessellation.area
streets["length"] = streets.length
        
buildings['eri'] = momepy.EquivalentRectangularIndex(buildings).series #generate urban form features 
buildings['elongation'] = momepy.Elongation(buildings).series
tessellation['convexity'] = momepy.Convexity(tessellation).series
streets["linearity"] = momepy.Linearity(streets).series
        
buildings["shared_walls"] = momepy.SharedWallsRatio(buildings).series
queen_1 = libpysal.weights.contiguity.Queen.from_dataframe(tessellation, ids="uID", silence_warnings=True)
tessellation["neighbors"] = momepy.Neighbors(tessellation, queen_1, "uID", weighted=True, verbose=False).series
tessellation["covered_area"] = momepy.CoveredArea(tessellation, queen_1, "uID", verbose=False).series
        
with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    buildings["neighbor_distance"] = momepy.NeighborDistance(buildings, queen_1, "uID", verbose=False).series
    queen_3 = momepy.sw_high(k=3, weights=queen_1)
    buildings_q1 = libpysal.weights.contiguity.Queen.from_dataframe(buildings, silence_warnings=True)
    buildings['interbuilding_distance'] = momepy.MeanInterbuildingDistance(buildings, queen_1, 'uID', queen_3, verbose=False).series
    buildings['adjacency'] = momepy.BuildingAdjacency(buildings, queen_3, 'uID', buildings_q1, verbose=False).series
    profile = momepy.StreetProfile(streets, buildings)
    streets["width"] = profile.w
    streets["width_deviation"] = profile.wd
    streets["openness"] = profile.o
    tessellation['car'] = momepy.AreaRatio(tessellation, buildings, 'area', 'area', 'uID').series
    graph = momepy.gdf_to_nx(streets)
    graph = momepy.node_degree(graph)
    graph = momepy.closeness_centrality(graph, radius=400, distance="mm_len")
    graph = momepy.meshedness(graph, radius=400, distance="mm_len")
    nodes, streets = momepy.nx_to_gdf(graph)
    buildings["nodeID"] = momepy.get_node_id(buildings, nodes, streets, "nodeID", "nID")
    merged = tessellation.merge(buildings.drop(columns=['nID', 'geometry']), on='uID')
    merged = merged.merge(streets.drop(columns='geometry'), on='nID', how='left')
    merged = merged.merge(nodes.drop(columns='geometry'), on='nodeID', how='left')
    
    temporary_df=pd.DataFrame(merged.median()).transpose()
    temporary_df['MSOA11CD'] = msoa_name
    temporary_df = temporary_df[['MSOA11CD','area_x', 'convexity', 'neighbors', 'covered_area', 'car', 'area_y',
                                         'eri', 'elongation', 'shared_walls', 'neighbor_distance',
                                         'interbuilding_distance', 'adjacency', 'length', 'linearity', 'width',
                                         'width_deviation', 'openness', 'degree', 'closeness', 'meshedness']]

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\geopandas\array.py:1406: UserWarning: CRS not set for some of the concatenation inputs. Setting output's CRS as OSGB36 / British National Grid (the single non-null crs provided).
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:278: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  objects.loc[mask, objects.geometry.name] = objects[mask].buffer(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\geopandas\tools\clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:384: UserWarning: Tessellation does not fully match buildings. 1 element(s) collapsed during generation - unique_id: {1236}
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:395: UserWarning: Tessellation contains MultiPolygon elements. Initial objects should be edited. unique_id of affected elements: [1799, 405, 655, 1719, 446, 654, 663, 445, 317, 638, 633, 634, 640, 631, 796, 799, 815, 551, 671, 1786, 723, 552, 174, 1796, 1459, 1787, 1797, 1782, 842, 1450, 621, 1785, 1403, 291, 188, 1829, 1830, 181, 1461, 189, 947, 1466, 179, 1744, 629, 844, 1922, 1924, 851, 857, 860, 343, 1165, 1736, 644, 1628, 274, 1278, 1938, 591, 1937, 1276, 1270, 134, 1267, 1269, 142, 1629, 1811, 270, 1886, 1810, 1123, 1122, 1129, 1130, 1124, 1112, 1232, 1930, 229, 1912, 1929, 1923, 1458, 1317, 1045, 87, 297, 1365, 1368, 1360, 95, 1500, 570, 573, 571, 1058, 98, 1869, 1792]
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\distribution.py:893: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.
  gdf.set_index(unique_id).geometry.iteritems(),

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In [12]:
buildings.head(1)
Out[12]:
geometry uID MSOA11CD nID area eri elongation shared_walls neighbor_distance interbuilding_distance adjacency nodeID
0 POLYGON ((531541.552 181119.077, 531541.397 18... 0 E02000001 230 681.10988 1.001729 0.5879 0.0 10.15555 9.839758 0.393939 182
In [13]:
buildings.explore() #explore all buildings in central london (city of london)
Out[13]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [14]:
streets.explore()
Out[14]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [15]:
tessellation.explore()
Out[15]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [16]:
blocks = momepy.Blocks(tessellation, streets, buildings, 'bID', 'uID')
blocks.blocks.head()
blocks.blocks.explore()
Out[16]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [17]:
f, ax = plt.subplots(figsize=(10, 10))
buildings.plot('area', ax=ax, legend=True, scheme='quantiles', cmap='Blues',
               legend_kwds={'loc': 'lower left'})
ax.set_axis_off()
plt.show()
In [18]:
fig, ax = plt.subplots(1, 2, figsize=(24, 12))

buildings.plot("eri", ax=ax[0], scheme="natural_breaks", legend=True)
buildings.plot("elongation", ax=ax[1], scheme="natural_breaks", legend=True)

ax[0].set_axis_off()
ax[1].set_axis_off()

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
In [19]:
fig, ax = plt.subplots(1, 2, figsize=(24, 12))

tessellation.plot("convexity", ax=ax[0], scheme="natural_breaks", legend=True)
streets.plot("linearity", ax=ax[1], scheme="natural_breaks", legend=True)

ax[0].set_axis_off()
ax[1].set_axis_off()

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.
  warnings.warn(
In [20]:
fig, ax = plt.subplots(1, 2, figsize=(24, 12))

buildings.plot("neighbor_distance", ax=ax[0], scheme="natural_breaks", legend=True)
tessellation.plot("covered_area", ax=ax[1], scheme="natural_breaks", legend=True)

ax[0].set_axis_off()
ax[1].set_axis_off()

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
In [21]:
fig, ax = plt.subplots(1, 2, figsize=(24, 12))

buildings.plot("interbuilding_distance", ax=ax[0], scheme="natural_breaks", legend=True)
buildings.plot("adjacency", ax=ax[1], scheme="natural_breaks", legend=True)

ax[0].set_axis_off()
ax[1].set_axis_off()

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=8.
  warnings.warn(
In [22]:
fig, ax = plt.subplots(1, 3, figsize=(24, 12))

streets.plot("width", ax=ax[0], scheme="natural_breaks", legend=True) #street width
streets.plot("width_deviation", ax=ax[1], scheme="natural_breaks", legend=True) #street width deviation
streets.plot("openness", ax=ax[2], scheme="natural_breaks", legend=True) #street openness

ax[0].set_axis_off()
ax[1].set_axis_off()
ax[2].set_axis_off()

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\sklearn\cluster\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=5.
  warnings.warn(
In [33]:
#Street Alignment

streets_graph = osmnx.graph_from_polygon(msoa, network_type='drive')
streets_graph= osmnx.projection.project_graph(streets_graph, to_crs='EPSG:27700')
edges = osmnx.graph_to_gdfs(streets_graph, nodes=False, edges=True,
                                   node_geometry=False, fill_edge_geometry=True)
edges['networkID'] = momepy.unique_id(edges)
buildings['networkID'] = momepy.get_network_id(buildings, edges,
                                               'networkID').values

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C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:760: UserWarning: Some objects were not attached to the network. Set larger min_size. 36 affected elements
  warnings.warn(
In [23]:
buildings['orientation'] = momepy.Orientation(buildings).series
tessellation['orientation'] = momepy.Orientation(tessellation).series

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In [24]:
##cell alignment
buildings.plot(column='orientation',legend=True,legend_kwds={"shrink":.3}, cmap='Spectral', 
               figsize=(10, 10)).set_axis_off() #building solar orientation
In [25]:
##cell alignment
tessellation.plot(column='orientation',legend=True,legend_kwds={"shrink":.3}, cmap='Spectral',
               figsize=(10, 10)).set_axis_off() #tesellation orientation
In [31]:
blg_cell_align = momepy.CellAlignment(buildings, tessellation,
                                      'orientation', 'orientation',
                                      'uID', 'uID')
buildings['cell_align'] = blg_cell_align.series #generating cell alignment metric
In [32]:
buildings.plot(column='cell_align',legend=True,legend_kwds={"shrink":.3}, cmap='Spectral',
               figsize=(10, 10)).set_axis_off() #calculating cell alignment building orientation - tessellation orientation
In [34]:
buildings_net = buildings.dropna(subset=["networkID"])
buildings_net.plot(figsize=(10, 10)).set_axis_off()
In [35]:
str_align = momepy.StreetAlignment(buildings_net, edges,
                                   'orientation', 'networkID',
                                   'networkID')
buildings_net['str_align'] = str_align.series

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\geopandas\geodataframe.py:1443: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)
In [36]:
ax = edges.plot(color='grey', linewidth=0.5, figsize=(10, 10))
buildings_net.plot(ax=ax, column='str_align', legend=True)
ax.set_axis_off()
In [37]:
print(eng_df.iloc[x].geometry.centroid)

POINT (-0.0923889468524288 51.514381323272744)
In [58]:
import osmnx as ox
point = (51.514381323272744, -0.0923889468524288)
dist = 2000
gdf = ox.geometries.geometries_from_point(point, dist=dist, tags={'building':True})
buildings = ox.projection.project_gdf(gdf)
buildings = buildings[buildings.geom_type.isin(['Polygon', 'MultiPolygon'])]

buildings['uID'] = momepy.unique_id(buildings)
limit = momepy.buffered_limit(buildings)
tessellation = momepy.Tessellation(buildings, unique_id='uID', limit=limit).tessellation

def clean_heights(x):
    try:
        return float(x)
    except ValueError:
        return 0

buildings['height'] = buildings['height'].fillna(0).apply(clean_heights)
buildings = buildings.reset_index().explode()
buildings.reset_index(inplace=True, drop=True)
streets_graph = ox.graph_from_point(point, dist, network_type='drive')
streets_graph = ox.projection.project_graph(streets_graph)
edges = ox.graph_to_gdfs(streets_graph, nodes=False, edges=True,
                                   node_geometry=False, fill_edge_geometry=True)

Inward offset...

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:278: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  objects.loc[mask, objects.geometry.name] = objects[mask].buffer(

Generating input point array...
Generating Voronoi diagram...
Generating GeoDataFrame...
Dissolving Voronoi polygons...

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\geopandas\tools\clip.py:67: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
  clipped.loc[
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:384: UserWarning: Tessellation does not fully match buildings. 6 element(s) collapsed during generation - unique_id: {8455, 10291, 10293, 10296, 13627, 13629}
  warnings.warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\momepy\elements.py:395: UserWarning: Tessellation contains MultiPolygon elements. Initial objects should be edited. unique_id of affected elements: [1936, 385, 1327, 14406, 498, 14522, 10512, 5458, 5461, 12138, 12600, 12599, 8454, 5619, 8340, 8338, 8788, 8783, 7994, 12345, 962, 10788, 1357, 5516, 8553, 13898, 13233, 13234, 4134, 6470, 6473, 6489, 11186, 4138, 11187, 5489, 8551, 14510, 14509, 8545, 1011, 8542, 8544, 1027, 14426, 3522, 10683, 11809, 7872, 7871, 7879, 7873, 7878, 7861, 8354, 14471, 8725, 508, 4285, 506, 519, 8913, 9177, 9180, 10242, 516, 5378, 5386, 7755, 7768, 5380, 13620, 12362, 14463, 11842, 11845, 11843, 843, 11829, 5849, 14537, 5850, 5829, 5842, 751, 11821, 11823, 2756, 11813, 5448, 5423, 660, 11677, 8203, 14469, 14114, 2237, 14113, 14439, 7777, 5168, 12437, 10280, 157, 12136, 1795, 1852, 12368, 14530, 1681, 5073, 13973, 13972, 13631, 10233, 14515, 13593, 13560, 13559, 9428, 8752, 9427, 13589, 13588, 13590, 13623, 13979, 13624, 14549, 13981, 13980, 10251, 13982, 10279, 13983, 2075, 2074, 13592, 13586, 14542, 14498, 8885, 8886, 8910, 7775, 14474, 14475, 8309, 5941, 5947, 5938, 5951, 5945, 5940, 5962, 5074, 11638, 11612, 4449, 7940, 5209, 5961, 5970, 5208, 1990, 13417, 13418, 10015, 1991, 6979, 10030, 9868, 10089, 13521, 7649, 12377, 1875, 10013, 5699, 11653, 1903, 1905, 12378, 12279, 6534, 6516, 6518, 14452, 6525, 14454, 14453, 10012, 6531, 9390, 9389, 9392, 8486, 12333, 11871, 8950, 8949, 9019, 9021, 9016, 9017, 9014, 9015, 6015, 12335, 4193, 5328, 6079, 5329, 12334, 9599, 12336, 5691, 9981, 8995, 8996, 8999, 11342, 10159, 11340, 10158, 11337, 11338, 14534, 9119, 9363, 23, 9353, 9358, 9137, 9149, 9154, 9153, 12492, 9237, 6273, 6259, 7481, 7545, 7546, 7476, 7474, 7557, 6250, 6226, 14526, 11924, 7569, 7127, 7139, 7652, 7164, 10643, 12505, 12382, 4269, 9301]
  warnings.warn(
C:\Users\neetm\AppData\Local\Temp\ipykernel_17432\697467702.py:19: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.
  buildings = buildings.reset_index().explode()
In [59]:
ax = buildings.plot(figsize=(10, 10), color='lightgrey')
edges.plot(ax=ax)
ax.set_axis_off()
In [60]:
profile = momepy.StreetProfile(edges, buildings, heights='height')
In [61]:
edges['widths'] = profile.w
edges['width_deviations'] = profile.wd
edges['openness'] = profile.o
edges['heights'] = profile.h
edges['heights_deviations'] = profile.hd
edges['profile'] = profile.p
In [62]:
f, axes = plt.subplots(figsize=(15, 25), ncols=2, nrows=3)
edges.plot(ax=axes[0][0], column='widths', legend=True, cmap='Blues_r')
buildings.plot(ax=axes[0][0], color='lightgrey')
edges.plot(ax=axes[0][1], column='width_deviations', legend=True)
buildings.plot(ax=axes[0][1], color='lightgrey')
axes[0][0].set_axis_off()
axes[0][0].set_title('widths')
axes[0][1].set_axis_off()
axes[0][1].set_title('width_deviations')
edges.plot(ax=axes[1][0], column='profile', legend=True, cmap='Spectral')
buildings.plot(ax=axes[1][0], color='lightgrey')
edges.plot(ax=axes[1][1], column='openness', legend=True, cmap='Greens')
buildings.plot(ax=axes[1][1], color='lightgrey')
axes[1][0].set_axis_off()
axes[1][0].set_title('profile')
axes[1][1].set_axis_off()
axes[1][1].set_title('openness')
edges.plot(ax=axes[2][0], column='heights', legend=True, cmap='Reds')
buildings.plot(ax=axes[2][0], color='lightgrey')
edges.plot(ax=axes[2][1], column='heights_deviations', legend=True)
buildings.plot(ax=axes[2][1], color='lightgrey')
axes[2][0].set_axis_off()
axes[2][0].set_title('heights')
axes[2][1].set_axis_off()
axes[2][1].set_title('heights_deviations')
plt.show()
In [63]:
f, ax = plt.subplots(figsize=(10, 10))
tessellation.plot(ax=ax)
buildings.plot(ax=ax, color='white', alpha=.5)
ax.set_axis_off()
plt.show()
In [70]:
blg_cc = momepy.CircularCompactness(buildings)
buildings['circular_com'] = blg_cc.series #Circular compactness measures the ratio of object area to the area of its smallest circumsribed circle:
In [71]:
f, ax = plt.subplots(figsize=(10, 10))
buildings.plot(ax=ax, column='circular_com', legend=True, cmap='viridis')
ax.set_axis_off()
plt.show()

measuring density¶

In [64]:
tessellation['area'] = momepy.Area(tessellation).series #tessellation area
buildings['area'] = momepy.Area(buildings).series
tess_car = momepy.AreaRatio(tessellation, buildings, 'area', 'area', 'uID') #tessellation area ratio
tessellation['CAR'] = tess_car.series #coverage ratio
In [65]:
tess_car = momepy.AreaRatio(tessellation, buildings,
                            momepy.Area(tessellation).series,
                            momepy.Area(buildings).series, 'uID')
tessellation['CAR'] = tess_car.series
In [66]:
f, ax = plt.subplots(figsize=(10, 10))
tessellation.plot(ax=ax, column='CAR', legend=True, scheme='quantiles', k=10, cmap='viridis')
buildings.plot(ax=ax, color='white', alpha=0.5)
ax.set_axis_off()
plt.show() #coverage area ratio
In [ ]:
def clean_heights(x):
    try:
        return float(x)
    except ValueError:
        return 0

buildings['height'] = buildings['height'].fillna(0).apply(clean_heights)

buildings['floor_area'] = momepy.FloorArea(buildings, 'height').series
In [68]:
tessellation['FAR'] = momepy.AreaRatio(tessellation, buildings,
                                       'area', 'floor_area', 'uID').series #floor area ratio
In [69]:
f, ax = plt.subplots(figsize=(10, 10))
tessellation.plot(ax=ax, column='FAR', legend=True, scheme='quantiles', k=10, cmap='viridis')
buildings.plot(ax=ax, color='white', alpha=0.5)
ax.set_axis_off()
plt.show()

C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\mapclassify\classifiers.py:238: UserWarning: Warning: Not enough unique values in array to form k classes
  Warn(
C:\Users\neetm\anaconda3\envs\geopandas_env\Lib\site-packages\mapclassify\classifiers.py:241: UserWarning: Warning: setting k to 2
  Warn("Warning: setting k to %d" % k_q, UserWarning)

import data for Urban Form via Parquet Files¶

In [ ]:
import pandas as pd
import matplotlib as plt
import os
import osmnx
import dask
import dask.dataframe as dd
import glob
from dask.distributed import Client
import fastparquet
import geopandas as gpd
import dask_geopandas
In [ ]:
path = r'C:\Users\neetm\Desktop\Dissertation\Data\MSOAs'
urban1 = dask_geopandas.read_parquet(path + f'/msoas_urbanform_med.pq')
In [6]:
urban1[urban1['msoa11cd']=='E02000001']
Out[6]:
Dask-GeoPandas GeoDataFrame Structure:
msoa11cd sdbAre sdbHei sdbPer sdbVol ssbFoF ssbVFR sdbCoA ssbCCo ssbCor ssbSqu ssbERI ssbElo ssbCCM ssbCCD stbOri sdcLAL sdcAre sicFAR sscCCo sscERI stcOri sicCAR stbCeA mtbAli mtbNDi mtcWNe mdcAre ltcWRE ltbIBD ltcRea ltcAre sdsSPW sdsSPH sdsSPO sdsSWD sdsSHD sdsSPR sdsLen sssLin ldsMSL mtdDeg lcdMes linP3W linP4W linPDE lcnClo ldsCDL xcnSCl mtdMDi lddNDe linWID ldeAre ldePer lseCCo lseERI lseCWA lteOri lteWNB lieWCe stbSAl ldbPWL mtbSWR sddAre sdsAre sisBpM misCel mdsAre lisCel ldsAre geometry
npartitions=1
object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 geometry
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Dask Name: getitem, 4 tasks
In [8]:
path = r'C:\Users\neetm\Desktop\Dissertation\Data\MSOAs'
urban2 = dask_geopandas.read_parquet(path + f'/msoas_urbanform_med.pq')

C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\dask\dataframe\io\parquet\arrow.py:1903: FutureWarning: 'ParquetDataset.partitions' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.partitioning' attribute instead.
  if dataset.partitions is not None:
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\dask\dataframe\io\parquet\arrow.py:1931: FutureWarning: 'ParquetDataset.metadata' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version.
  if dataset.metadata:
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\dask\dataframe\io\parquet\arrow.py:1950: FutureWarning: 'ParquetDataset.schema' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.schema' attribute instead (which will return an Arrow schema instead of a Parquet schema).
  if dataset.schema is not None:
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\dask\dataframe\io\parquet\arrow.py:1978: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
  if len(dataset.pieces) > 1:
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\dask\dataframe\io\parquet\arrow.py:1990: FutureWarning: 'ParquetDataset.pieces' attribute is deprecated as of pyarrow 5.0.0 and will be removed in a future version. Specify 'use_legacy_dataset=False' while constructing the ParquetDataset, and then use the '.fragments' attribute instead.
  for piece, fn in zip(dataset.pieces, fns):
In [9]:
urban2.head()
Out[9]:
msoa11cd sdbAre sdbHei sdbPer sdbVol ssbFoF ssbVFR sdbCoA ssbCCo ssbCor ... misCel mdsAre lisCel ldsAre objectid msoa11nm msoa11nmw st_areasha st_lengths geometry
0 E02000001 136.281525 23.1 58.283511 2974.246398 0.700039 2.390425 0.0 0.449578 6.0 ... 29.000000 49620.352109 111.122772 227273.375613 1 City of London 001 City of London 001 2.905399e+06 9024.059703 MULTIPOLYGON (((532136.950 182198.368, 532160....
1 E02000002 50.414375 7.5 29.718539 391.234663 0.980618 1.726172 0.0 0.567912 4.0 ... 67.195382 39354.746259 223.000000 175858.425524 2 Barking and Dagenham 001 Barking and Dagenham 001 2.165634e+06 8152.697593 POLYGON ((548883.435 190845.558, 548882.997 19...
2 E02000003 56.730325 8.5 33.221816 478.474295 0.943607 1.719994 0.0 0.527822 6.0 ... 88.000000 33209.520648 298.271847 149024.773835 3 Barking and Dagenham 002 Barking and Dagenham 002 2.143565e+06 9118.449453 POLYGON ((549104.294 189324.880, 548956.354 18...
3 E02000004 52.297068 7.8 30.696216 402.000240 0.982960 1.692187 0.0 0.540299 4.0 ... 118.520702 43289.679552 376.186231 183850.215778 4 Barking and Dagenham 003 Barking and Dagenham 003 2.490215e+06 8207.610394 POLYGON ((551551.892 187364.909, 551479.898 18...
4 E02000005 47.146250 8.3 29.397671 391.262617 0.893687 1.610037 0.0 0.557735 6.0 ... 131.000000 44284.845682 586.000000 221713.532750 5 Barking and Dagenham 004 Barking and Dagenham 004 1.186180e+06 6964.961665 POLYGON ((549101.500 187656.302, 549163.242 18...

5 rows × 76 columns

In [10]:
urban2.columns.to_list()
Out[10]:
['msoa11cd',
 'sdbAre',
 'sdbHei',
 'sdbPer',
 'sdbVol',
 'ssbFoF',
 'ssbVFR',
 'sdbCoA',
 'ssbCCo',
 'ssbCor',
 'ssbSqu',
 'ssbERI',
 'ssbElo',
 'ssbCCM',
 'ssbCCD',
 'stbOri',
 'sdcLAL',
 'sdcAre',
 'sicFAR',
 'sscCCo',
 'sscERI',
 'stcOri',
 'sicCAR',
 'stbCeA',
 'mtbAli',
 'mtbNDi',
 'mtcWNe',
 'mdcAre',
 'ltcWRE',
 'ltbIBD',
 'ltcRea',
 'ltcAre',
 'sdsSPW',
 'sdsSPH',
 'sdsSPO',
 'sdsSWD',
 'sdsSHD',
 'sdsSPR',
 'sdsLen',
 'sssLin',
 'ldsMSL',
 'mtdDeg',
 'lcdMes',
 'linP3W',
 'linP4W',
 'linPDE',
 'lcnClo',
 'ldsCDL',
 'xcnSCl',
 'mtdMDi',
 'lddNDe',
 'linWID',
 'ldeAre',
 'ldePer',
 'lseCCo',
 'lseERI',
 'lseCWA',
 'lteOri',
 'lteWNB',
 'lieWCe',
 'stbSAl',
 'ldbPWL',
 'mtbSWR',
 'sddAre',
 'sdsAre',
 'sisBpM',
 'misCel',
 'mdsAre',
 'lisCel',
 'ldsAre',
 'objectid',
 'msoa11nm',
 'msoa11nmw',
 'st_areasha',
 'st_lengths',
 'geometry']
In [14]:
urban_mean = gpd.GeoDataFrame(urban1)
In [16]:
urban_mean.columns = ['msoa11cd',
 'sdbAre',
 'sdbHei',
 'sdbPer',
 'sdbVol',
 'ssbFoF',
 'ssbVFR',
 'sdbCoA',
 'ssbCCo',
 'ssbCor',
 'ssbSqu',
 'ssbERI',
 'ssbElo',
 'ssbCCM',
 'ssbCCD',
 'stbOri',
 'sdcLAL',
 'sdcAre',
 'sicFAR',
 'sscCCo',
 'sscERI',
 'stcOri',
 'sicCAR',
 'stbCeA',
 'mtbAli',
 'mtbNDi',
 'mtcWNe',
 'mdcAre',
 'ltcWRE',
 'ltbIBD',
 'ltcRea',
 'ltcAre',
 'sdsSPW',
 'sdsSPH',
 'sdsSPO',
 'sdsSWD',
 'sdsSHD',
 'sdsSPR',
 'sdsLen',
 'sssLin',
 'ldsMSL',
 'mtdDeg',
 'lcdMes',
 'linP3W',
 'linP4W',
 'linPDE',
 'lcnClo',
 'ldsCDL',
 'xcnSCl',
 'mtdMDi',
 'lddNDe',
 'linWID',
 'ldeAre',
 'ldePer',
 'lseCCo',
 'lseERI',
 'lseCWA',
 'lteOri',
 'lteWNB',
 'lieWCe',
 'stbSAl',
 'ldbPWL',
 'mtbSWR',
 'sddAre',
 'sdsAre',
 'sisBpM',
 'misCel',
 'mdsAre',
 'lisCel',
 'ldsAre',
 'geometry']
In [18]:
urban_mean.to_csv('urbanmean.csv')
In [11]:
urban2.columns.to_list()
Out[11]:
['msoa11cd',
 'sdbAre',
 'sdbHei',
 'sdbPer',
 'sdbVol',
 'ssbFoF',
 'ssbVFR',
 'sdbCoA',
 'ssbCCo',
 'ssbCor',
 'ssbSqu',
 'ssbERI',
 'ssbElo',
 'ssbCCM',
 'ssbCCD',
 'stbOri',
 'sdcLAL',
 'sdcAre',
 'sicFAR',
 'sscCCo',
 'sscERI',
 'stcOri',
 'sicCAR',
 'stbCeA',
 'mtbAli',
 'mtbNDi',
 'mtcWNe',
 'mdcAre',
 'ltcWRE',
 'ltbIBD',
 'ltcRea',
 'ltcAre',
 'sdsSPW',
 'sdsSPH',
 'sdsSPO',
 'sdsSWD',
 'sdsSHD',
 'sdsSPR',
 'sdsLen',
 'sssLin',
 'ldsMSL',
 'mtdDeg',
 'lcdMes',
 'linP3W',
 'linP4W',
 'linPDE',
 'lcnClo',
 'ldsCDL',
 'xcnSCl',
 'mtdMDi',
 'lddNDe',
 'linWID',
 'ldeAre',
 'ldePer',
 'lseCCo',
 'lseERI',
 'lseCWA',
 'lteOri',
 'lteWNB',
 'lieWCe',
 'stbSAl',
 'ldbPWL',
 'mtbSWR',
 'sddAre',
 'sdsAre',
 'sisBpM',
 'misCel',
 'mdsAre',
 'lisCel',
 'ldsAre',
 'objectid',
 'msoa11nm',
 'msoa11nmw',
 'st_areasha',
 'st_lengths',
 'geometry']
In [12]:
urban_median = gpd.GeoDataFrame(urban2)
In [13]:
urban_median.head()

C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\core\dtypes\inference.py:387: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  iter(obj)  # Can iterate over it.
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\core\dtypes\inference.py:388: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  len(obj)  # Has a length associated with it.
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\io\formats\printing.py:117: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  s = iter(seq)
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\io\formats\printing.py:121: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  for i in range(min(nitems, len(seq)))
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\io\formats\printing.py:125: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  if nitems < len(seq):
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\core\dtypes\inference.py:387: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  iter(obj)  # Can iterate over it.
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\core\dtypes\inference.py:388: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  len(obj)  # Has a length associated with it.
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\io\formats\printing.py:117: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
  s = iter(seq)
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\io\formats\printing.py:121: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  for i in range(min(nitems, len(seq)))
C:\Users\neetm\anaconda3\envs\geopandas_env\lib\site-packages\pandas\io\formats\printing.py:125: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.
  if nitems < len(seq):
Out[13]:
0 1 2 3 4 5 6 7 8 9 ... 66 67 68 69 70 71 72 73 74 75
0 E02000001 136.281525 23.1 58.283511 2974.246398 0.700039 2.390425 0.0 0.449578 6.0 ... 29.0 49620.352109 111.122772 227273.375613 1 City of London 001 City of London 001 2905399.307766 9024.059703 (POLYGON ((532136.9496997485 182198.3681596624...
1 E02000002 50.414375 7.5 29.718539 391.234663 0.980618 1.726172 0.0 0.567912 4.0 ... 67.195382 39354.746259 223.0 175858.425524 2 Barking and Dagenham 001 Barking and Dagenham 001 2165633.766619 8152.697593 POLYGON ((548883.4350340335 190845.5575863339,...
2 E02000003 56.730325 8.5 33.221816 478.474295 0.943607 1.719994 0.0 0.527822 6.0 ... 88.0 33209.520648 298.271847 149024.773835 3 Barking and Dagenham 002 Barking and Dagenham 002 2143564.999322 9118.449453 POLYGON ((549104.2941500901 189324.8800520888,...
3 E02000004 52.297068 7.8 30.696216 402.00024 0.98296 1.692187 0.0 0.540299 4.0 ... 118.520702 43289.679552 376.186231 183850.215778 4 Barking and Dagenham 003 Barking and Dagenham 003 2490215.446162 8207.610394 POLYGON ((551551.8921626799 187364.9086353246,...
4 E02000005 47.14625 8.3 29.397671 391.262617 0.893687 1.610037 0.0 0.557735 6.0 ... 131.0 44284.845682 586.0 221713.53275 5 Barking and Dagenham 004 Barking and Dagenham 004 1186179.92716 6964.961665 POLYGON ((549101.500199597 187656.3016682651, ...

5 rows × 76 columns

In [16]:
urban_median.columns = ['msoa11cd',
 'sdbAre',
 'sdbHei',
 'sdbPer',
 'sdbVol',
 'ssbFoF',
 'ssbVFR',
 'sdbCoA',
 'ssbCCo',
 'ssbCor',
 'ssbSqu',
 'ssbERI',
 'ssbElo',
 'ssbCCM',
 'ssbCCD',
 'stbOri',
 'sdcLAL',
 'sdcAre',
 'sicFAR',
 'sscCCo',
 'sscERI',
 'stcOri',
 'sicCAR',
 'stbCeA',
 'mtbAli',
 'mtbNDi',
 'mtcWNe',
 'mdcAre',
 'ltcWRE',
 'ltbIBD',
 'ltcRea',
 'ltcAre',
 'sdsSPW',
 'sdsSPH',
 'sdsSPO',
 'sdsSWD',
 'sdsSHD',
 'sdsSPR',
 'sdsLen',
 'sssLin',
 'ldsMSL',
 'mtdDeg',
 'lcdMes',
 'linP3W',
 'linP4W',
 'linPDE',
 'lcnClo',
 'ldsCDL',
 'xcnSCl',
 'mtdMDi',
 'lddNDe',
 'linWID',
 'ldeAre',
 'ldePer',
 'lseCCo',
 'lseERI',
 'lseCWA',
 'lteOri',
 'lteWNB',
 'lieWCe',
 'stbSAl',
 'ldbPWL',
 'mtbSWR',
 'sddAre',
 'sdsAre',
 'sisBpM',
 'misCel',
 'mdsAre',
 'lisCel',
 'ldsAre',
 'objectid',
 'msoa11nm',
 'msoa11nmw',
 'st_areasha',
 'st_lengths',
 'geometry']
In [17]:
urban_median.to_csv('urbanmedian.csv') #export to csv to be used in next python code including with all machine learning